class_docstr: A 3x3 convolution layer with some utility functions.
class_docstr_tok: ["A","3x3","convolution","layer","with","some","utility","functions","."]
code: def forward(self, x):
      # You might be thinking, "but what about padding?"
      # Answer: Tiling is used to stitch everything back together, though you could pad the image before providing it.
      return x.conv2d(self.W).add(self.b)
  
file_path: tinygrad/examples/vgg7_helpers/waifu2x.py
imports: import numpy
  from tinygrad.tensor import Tensor
  from PIL import Image
  from tinygrad.helpers import fetch
label_desc: Use image node_id 2 for calling the Conv3x3Biased obj's underlying member method code with example usage: obj.forward(x) and returns: x